Event Detection from Video Surveillance Data Based on Optical Flow Histogram and High-level Feature Extraction

2009 ◽  
Author(s):  
Ali Wali ◽  
Adel M. Alimi
Keyword(s):  
Feature Extraction ◽  
Optical Flow ◽  
Video Surveillance ◽  
Event Detection ◽  
Surveillance Data ◽  
High Level ◽  
High Level Feature

The application of graph diffusion in high-level feature extraction

2013 ◽  
Author(s):  
Xiaohan Du ◽  
Honggang Zhang ◽  
Jun Guo ◽  
Xiaojun Xu
Keyword(s):  
Feature Extraction ◽  
High Level ◽  
High Level Feature

High-level feature extraction in JPEG compressed domain

2004 ◽  
Author(s):  
C. K. Narayanan ◽  
M. C. Prakash ◽  
G. V. Prabhakara Rao
Keyword(s):  
Feature Extraction ◽  
Compressed Domain ◽  
High Level ◽  
High Level Feature

scholarly journals A Hybrid Technique using CNN+LSTM for Speech Emotion Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 1126-1130
Keyword(s):  
Feature Extraction ◽  
Human Computer Interaction ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Hybrid Technique ◽  
Proposed Model ◽  
High Level ◽  
High Level Feature ◽  
Convolutional Lstm

Automatic speech emotion recognition is a very necessary activity for effective human-computer interaction. This paper is motivated by using spectrograms as inputs to the hybrid deep convolutional LSTM for speech emotion recognition. In this study, we trained our proposed model using four convolutional layers for high-level feature extraction from input spectrograms, LSTM layer for accumulating long-term dependencies and finally two dense layers. Experimental results on the SAVEE database shows promising performance. Our proposed model is highly capable as it obtained an accuracy of 94.26%.

High-level surveillance event detection

2017 ◽  
Vol 01 (01) ◽  
pp. 1630007
Author(s):  
Fabio Persia ◽  
Daniela D’Auria
Keyword(s):  
Video Surveillance ◽  
Event Detection ◽  
Terrorist Attacks ◽  
Surveillance Systems ◽  
Detection Systems ◽  
Whole Process ◽  
Video Frames ◽  
The World ◽  
Public Areas ◽  
High Level

Security has been raised at major public buildings in the most famous and crowded cities all over the world following the terrorist attacks of the last years, the latest one at the Promenade des Anglais in Nice. For that reason, video surveillance systems have become more and more essential for detecting and hopefully even prevent dangerous events in public areas. In this work, we present an overview of the evolution of high-level surveillance event detection systems along with a prototype for anomaly detection in video surveillance context. The whole process is described, starting from the video frames captured by sensors/cameras till at the end some well-known reasoning algorithms for finding potentially dangerous activities are applied.

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